I created 1 command file for each patche Inside I have one line for each itinerary
With Brieux naming convention : each line get a unique identifier simply simulation_# from 1 to number of itineraries ran.
To add repetitions (see forceeps_replicates analyses) there are
multiple possibilities :
- use different seed in the command file and then regroup simulations
together. For example simulation 1-10 for the first itinerary, 11-20 for
the second… (either by keeping very good track of what is done (and a
lot of loops when importing data) or by having a table that gives the
correspondances between simulation number and caracteristics of the
simulation.)
- change naming convention in forceeps (by modifying brieux scripts to
get our own)
# Set working directory for all chunks to parent directory
knitr::opts_knit$set(root.dir = "../../")
# Set global chunk options
knitr::opts_chunk$set(
echo = FALSE,
warning = FALSE,
message = FALSE,
fig.width = 8,
fig.height = 8,
fig.align = "center"
)
## # A tibble: 24 × 4
## folder plot_id simul simulation_id
## <chr> <chr> <chr> <int>
## 1 output-cmd_1.txt RETZ_00964_01 simulation_1 1
## 2 output-cmd_1.txt RETZ_00964_01 simulation_2 2
## 3 output-cmd_1.txt RETZ_00964_01 simulation_3 3
## 4 output-cmd_3.txt RETZ_01354_01 simulation_1 4
## 5 output-cmd_3.txt RETZ_01354_01 simulation_2 5
## 6 output-cmd_3.txt RETZ_01354_01 simulation_3 6
## 7 output-cmd_4.txt RETZ_00839_02 simulation_1 7
## 8 output-cmd_4.txt RETZ_00839_02 simulation_2 8
## 9 output-cmd_4.txt RETZ_00839_02 simulation_3 9
## 10 output-cmd_5.txt RETZ_01376_01 simulation_1 10
## # ℹ 14 more rows
## # A tibble: 24 × 4
## folder plot_id simul simulation_id
## <chr> <chr> <chr> <int>
## 1 output-cmd_1.txt RETZ_00964_01 simulation_1 1
## 2 output-cmd_1.txt RETZ_00964_01 simulation_2 2
## 3 output-cmd_1.txt RETZ_00964_01 simulation_3 3
## 4 output-cmd_3.txt RETZ_01354_01 simulation_1 4
## 5 output-cmd_3.txt RETZ_01354_01 simulation_2 5
## 6 output-cmd_3.txt RETZ_01354_01 simulation_3 6
## 7 output-cmd_4.txt RETZ_00839_02 simulation_1 7
## 8 output-cmd_4.txt RETZ_00839_02 simulation_2 8
## 9 output-cmd_4.txt RETZ_00839_02 simulation_3 9
## 10 output-cmd_5.txt RETZ_01376_01 simulation_1 10
## # ℹ 14 more rows
| Plot ID | Surface (ha) | Stand Type | Silviculture Species | Dominant Species 1 | Dominant Species 2 | Structure & Soil Cover | Basal Area (m²/ha) | Mean DBH (cm) | Median Age (years) |
|---|---|---|---|---|---|---|---|---|---|
| RETZ_00839_02 | 2.35 | FHETP | HET | HET | F | 19.0 | 22.5 | 42.0 | |
| RETZ_00964_01 | 11.90 | FCHPE | CHP | CHP | F | 0.0 | 7.5 | 12.0 | |
| RETZ_01056_02 | 1.50 | IHETI | HET | HET | ERS | I | 26.0 | 45.0 | 144.5 |
| RETZ_01201_06 | 0.80 | FHET1 | HET | HET | F | 22.5 | 12.5 | 42.0 | |
| RETZ_01252_01 | 15.47 | FHETM | HET | HET | F | 27.5 | 37.5 | 59.5 | |
| RETZ_01354_01 | 13.61 | FHETE | HET | HET | F | 0.0 | 7.5 | 12.0 | |
| RETZ_01376_01 | 5.91 | FHETM | HET | HET | F | 24.0 | 37.5 | 59.5 | |
| RETZ_01642_01 | 7.03 | ICHSI | CHS | CHS | P.S | I | 22.5 | 45.0 | 90.0 |
The table below summarizes the diversity of the selected plots in
terms of stand type and dominant species. This highlights the current
selection’s limited heterogeneity, suggesting the need to include a
broader range of patches and avoid relying solely on random selection to
ensure a more representative sample. (See l.32 in
Generate.R)
Also in subsequent analyses I considered each plot as equal but for applicable results :
The study evaluates three distinct silvicultural strategies:
*Note: The decline in basal area observed in simulation 3 (natural evolution) likely results from natural mortality, as the management strategy is set to no intervention (150_3_0.5_0%_FSyl-80).*
Over the 80-year simulation period, we calculate the following indicators:
Hill diversity indices (richness, Shannon, Simpson, Hill 0, Hill 1, Hill 2)
Harvested volume by scenario
Standing biomass total and by species
To create scenarios I combined different itineraries together.
To be able to conclude on different proportions effect and to take into account that the dynamic of one scenario can depend on which plot were chosen I repeated each scenarios 10 time. Exemple : for half clearcut, half no management, I randomly selected half of the plots for each itinerary and repeated this process 10 times to capture variability in the results.